2026-09-18T21:57:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/544582022-01-13T07:53:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Robert Friedman and Peter Szolovits.
Cuevas, Penelope H. (Penelope Huggins)
Harvard University--MIT Division of Health Sciences and Technology.
Harvard University--MIT Division of Health Sciences and Technology
2010-04-28T15:35:32Z
2010-04-28T15:35:32Z
2009
2009
http://hdl.handle.net/1721.1/54458
551181402
Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 12).
Telephone linked care (TLC) is a telehealth intervention that has been shown to be effective in a variety of clinical settings. TLC is an interactive computerized telephone system. The system 'speaks' to patients, asking them questions and understanding their responses. There is logic built into the calls, so that a patient's response to a question will dictate the next question that is asked. This serves to personalize the call for each patient, and makes the conversation more realistic. All of the patients' responses are stored in a database. This database provides much opportunity for analysis because a single phone call contains many responses. Visualization is an important way of gaining insight into data. Visualization can make data easier to understand and process. Different aspects of data can be encoded in a visualization. The TLC data lends itself to visualization. By viewing each of the questions that the system asks as nodes, and connecting the nodes by the chronological order in which these questions are asked, a tree structure will reveal the conversational paths that are taken in the calls. By combining data from multiple calls and encoding them in this tree structure, new insights can be gained into the TLC data. For example, the frequency with which questions are answered in a particular way can be encoded to reveal the most common pathways through the tree. This paper describes a visualization application of TLC data which will allow researchers to gain new insights into the TLC conversations and into medical interviews in general.
by Penelope H. Cuevas.
S.M.
24 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Harvard University--MIT Division of Health Sciences and Technology.
Visualizing the conversation pathways of telephone linked care in a directed graph
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>Telephone linked care (TLC) is a telehealth intervention that has been shown to be effective in a variety of clinical settings. TLC is an interactive computerized telephone system. The system 'speaks' to patients, asking them questions and understanding their responses. There is logic built into the calls, so that a patient's response to a question will dictate the next question that is asked. This serves to personalize the call for each patient, and makes the conversation more realistic. All of the patients' responses are stored in a database. This database provides much opportunity for analysis because a single phone call contains many responses. Visualization is an important way of gaining insight into data. Visualization can make data easier to understand and process. Different aspects of data can be encoded in a visualization. The TLC data lends itself to visualization. By viewing each of the questions that the system asks as nodes, and connecting the nodes by the chronological order in which these questions are asked, a tree structure will reveal the conversational paths that are taken in the calls. By combining data from multiple calls and encoding them in this tree structure, new insights can be gained into the TLC data. For example, the frequency with which questions are answered in a particular way can be encoded to reveal the most common pathways through the tree. This paper describes a visualization application of TLC data which will allow researchers to gain new insights into the TLC conversations and into medical interviews in general.</Abstract>
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